Abstract
In this paper, a fuzzy coordination method based on interaction prediction principle (IPP) and reinforcement learning is presented for the optimal control of robot manipulators with three degrees-of-freedom. For this purpose, the robot manipulator is considered as a two-level large-scale system where in the first level, the robot manipulator is decomposed into several subsystems. In the second level, a fuzzy interaction prediction system is introduced for coordination of the overall system where a critic vector is also used for evaluating its performance. The simulation results on using the proposed novel approach, for optimal control of robot manipulators show its effectiveness and superiority in comparison with the centralized optimization methods
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